As a devastating hazard, drought not only imposes extensive and long-term effects on the global environmental system, it also causes significant losses to the economy and human life (Mishra and Singh, 2010; Sternberg, 2011; Van Dijk et al., 2013). To reduce drought risk and prevent drought under global climate change and global warming (Trenberth et al., 2014), several research contributions have been made in the fields of drought monitoring (Aadhar and Mishra, 2017; West et al., 2019) and prediction (AghaKouchak, 2015; Hao et al., 2018, 2014). Those studies considered hydrological and meteorological variables to better understand regional drought event causes and quantification.
Other studies use ecological variables to identify this natural hazard at the ecosystem level (Anderegg et al., 2018; Banerjee et al., 2013; Schwalm et al., 2017; Van der Molen et al., 2011). In particular, drought recovery time (DRT) has received growing research attention in recent years (Ahmadi et al., 2019; Ahmadi and Moradkhani, 2019; He et al., 2018; Huang et al., 2021; Liu et al., 2019; Schwalm et al., 2017; Seneviratne and Ciais, 2017; Zhang et al., 2019). DRT will likely become longer than the time between drought events (Schwalm et al., 2017). Regional drought effects are compounded if a new drought event occurs before recovery from a preceding drought event is complete (Seneviratne and Ciais, 2017). Therefore, accurate DRT assessments are essential for understanding possible ecological risks.
DRT is defined as the time required for a region to fully return to its pre-drought conditions (Schwalm et al., 2017). Drought identification and recovery parameter selection are the key steps in DRT calculations. To identify a drought event, previous studies used the meteorological drought index (Standardized Precipitation Evapotranspiration Index, SPEI) and Drought Severity Index (DSI) (Liu et al., 2019; Schwalm et al., 2017; Yu et al., 2017). Climate data-based SPEI is easy to estimate for long-term analysis but it is not linked to plant condition (Vicente-Serrano et al., 2010). Satellite-based DSI includes greenness information with high spatial resolution. However, DSI data is only available from 2000 to 2011 with uncertainties from the satellite (cloud cover, atmospheric aerosols, and low solar illumination) and single input data (Mu et al., 2013).
Many factors such as water quantity (streamflow and total water storage), water quality (water temperature, turbidity, and dissolved oxygen), ecosystem fluxes (carbon and energy fluxes), and gross primary productivity (GPP) are used in drought recovery assessments (Ahmadi et al., 2019; He et al., 2018; Schwalm et al., 2017; Seneviratne and Ciais, 2017; Zhang et al., 2019). For example, the change in total water storage was used to identify hydrological DRT (between 3.6 and 5.7 months) for the Yangtze River in China (Zhang et al., 2019). Ahmadi et al. (2019) analyzed drought recovery by considering both streamflow and water quality parameter changes. They found that the average recovery time in the contiguous United States is around 1.2 months. The time required for carbon and energy fluxes to recover from the 2012 U.S. drought (0.5–2 months) and the 2003 European drought (1–2 months) were examined by He et al. (2018). Based on changes to GPP, global spatiotemporal DRT patterns were examined by Schwalm et al. (2017) and Yu et al. (2017). All of these studies focused on the DRT length and response functions. Among the factors, GPP is the most impactful due to its high drought sensitivity and spatiotemporal patterns. It can accommodate increasingly fine spatial resolution and frequent repeat measurements (Schwalm et al., 2017). Schwalm et al. (2017) estimated that DRT can be determined at 0.5° spatial resolution and 6 months of temporal resolution from 1901 to 2010 around the world. They found that most of the world can recover from a drought in less than six months. Unlike a conventional drought, a flash drought typically occurs during warm seasons and can occur more frequently - in one or two months (Ford and Labosier, 2017). Therefore, DRT should be examined in more detailed studies at a high spatiotemporal resolution. Yu et al. (2017) calculated global DRT from 2000 to 2011 at 0.5° spatial resolution and 1 month temporal resolution, but the results were different for Schwalm et al. (2017) in terms of spatial pattern and DRT length. As reported by Liu et al. (2019), using different methods to define drought events and recovery levels are the key factors contributing to the contradictory conclusions. Despite the large amount of research on meteorological and hydrological DRT, there has been very little work integrating long-term agricultural DRT at a high spatiotemporal resolution in dry regions such as East Africa, where people are largely dependent on rain-fed agriculture and livestock farming (Gebremeskel et al., 2019).
To better understand drought recovery in the Lake Victoria Basin, our objectives are 1) check the seasonal patterns and trends of drought-related variables; 2) capture drought events using meteorological (SPEI) and agricultural (vegetation condition index [VCI]) drought indices from 2003 to 2016; 3) investigate SPEI based- and VCI based-DRT for high spatiotemporal resolution; and 4) examine parameter importance for determining DRT across the Lake Victoria Basin. To the best of our knowledge, this is the first comprehensive study to quantify agricultural DRT at a high spatiotemporal resolution.